Improving Fabrication Fidelity of Integrated Nanophotonic Devices Using Deep Learning-Reference-Cited by-同舟云学术

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Improving Fabrication Fidelity of Integrated Nanophotonic Devices Using Deep Learning

Published:2023-06-06 Issue:6 Volume:10 Page:1953-1961
ISSN:2330-4022
Container-title:ACS Photonics
language:en
Short-container-title:ACS Photonics

Author:

Gostimirovic Dusan¹^ORCID,Grinberg Yuri²,Xu Dan-Xia³,Liboiron-Ladouceur Odile¹

Affiliation:

1. Department of Electrical and Computer Engineering, McGill University, 845 Sherbrooke Street West, Montreal H3A 0G4, Canada

2. Digital Technologies Research Centre, National Research Council Canada, 1200 Montreal Road, Ottawa K1A 0R6, Canada

3. Advanced Electronics and Photonics Research Centre, National Research Council Canada, 1200 Montreal Road, Ottawa K1A 0R6, Canada

Funder

National Research Council Canada

Publisher

American Chemical Society (ACS)

Subject

Electrical and Electronic Engineering,Atomic and Molecular Physics, and Optics,Biotechnology,Electronic, Optical and Magnetic Materials

Link

https://pubs.acs.org/doi/pdf/10.1021/acsphotonics.3c00389

Reference27 articles.

1. Review of Silicon Photonics Technology and Platform Development

2. Roadmap on silicon photonics

3. The commercialization of silicon photonics

4. Perspective on the future of silicon photonics and electronics

5. Computational Lithography for Silicon Photonics Design

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2. Genetic algorithm optimized microstructure to enhance waveguide light coupling efficiency at normal incidences;Applied Optics;2024-07-22

3. Towards subwavelength pixels: nanophotonic color routers for ultra-compact high-efficiency CMOS image sensors;Journal of Optics;2024-07-15

4. Advancing Silicon Photonics Through Machine Learning: From Device Design to Fabrication;2024 IEEE 24th International Conference on Nanotechnology (NANO);2024-07-08

5. Ultra-compact and efficient photonic waveguide bends with different configurations designed by topology optimization;Scientific Reports;2024-03-18

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